Household Air Pollution, Levels of Micronutrients and Heavy Metals in Cord and Maternal Blood, and Pregnancy Outcomes
Bibliographic record
Abstract
Cooking with kerosene emits toxic pollutants that may impact pregnancy outcomes. Sixty-eight women in their first trimester of pregnancy, kerosene users (n = 42) and liquefied natural gas (LNG) users (n = 26), were followed until birth. Maternal and cord blood were collected immediately after birth. Levels of micronutrients and heavy metals were quantified. Pregnancy outcomes (gestation age (GA), birth weight (BW), and chest and head circumference) were also measured. Mean (± standard deviation (SD)) age of mothers in kerosene and LNG groups were similar (p = 0.734). Mean (±SD) BW of newborns of LNG users was significantly higher compared to newborns of kerosene users (3.43 ± 0.32 vs. 3.02 ± 0.43, p < 0.001). Mean GA (in weeks) was similar between the two groups (p = 0.532). Women in the kerosene group had significantly higher cord blood levels of zinc, lead, mercury, iodine and vitamin B6 and lower levels of folic acid compared to LNG users (p < 0.05). Newborns of kerosene users had reduced levels of zinc, lead, mercury, iodine, vitamins B6 and B12, folic acid, and homocysteine compared with LNG users (p < 0.05). Also, cooking with kerosene was significantly associated with reduced birth weight after adjusting for potential confounders (β ± standard error (SE) = −0.326 ± 0.155; p = 0.040). Smoke from kerosene stove was associated with reduced birth weight and micronutrients imbalance in mothers and newborns.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".